Load Libraries and set a seed
library(Seurat)
library(ggplot2)
library(here)
library(dplyr)
set.seed(9)
Load data and reference
load(file = here("data/annotated.seurat.rds"))
# checking your cells is always a good place to start
integrated.seurat
An object of class Seurat
34285 features across 26254 samples within 2 assays
Active assay: RNA (32285 features, 3000 variable features)
9 layers present: counts.KZ1, counts.KZ2, counts.KZ3, counts.KZ4, data.KZ1, data.KZ2, data.KZ3, data.KZ4, scale.data
1 other assay present: mnn.reconstructed
10 dimensional reductions calculated: pca, umap.unintegrated, cca, umap.cca, rpca, umap.rpca, harmony, umap.harmony, mnn, umap.mnn
DimPlot(integrated.seurat, reduction = "umap.harmony", group.by = "prediction", label=TRUE)

DimPlot(integrated.seurat, reduction = "umap.harmony", group.by = "harmony.clusters", label=TRUE)

Subset out the clusters that are of the myeloid lineage
# lets refer back to the our cluster annotation plot
knitr::include_graphics(here("plots/4.annotation/ClusterAnnotation.png"))

# which clusters are myeloid cells?
myeloid <- subset(integrated.seurat, harmony.clusters %in% c("0", "6", "13", "17", "19"))
DimPlot(myeloid, reduction = "umap.harmony", group.by = "prediction", label=TRUE)

DimPlot(myeloid, reduction = "umap.harmony", group.by = "harmony.clusters", label=TRUE)

# re-cluster the subset (NOTE THE OBJECT CANNOT BE JOINED ALREADY OR IT WON'T PROPERLY CLUSTER)
DefaultAssay(myeloid) <- "RNA"
myeloid <- FindVariableFeatures(myeloid)
Finding variable features for layer counts.KZ1
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.KZ2
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.KZ3
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.KZ4
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
myeloid <- RunPCA(myeloid)
Warning in PrepDR5(object = object, features = features, layer = layer, :
The following features were not available: S100a10, Rabgap1l, Tubb4b, Airn, Cd2, Setbp1, Lat, Gimap6, Mllt3, Tcf4, Gimap4, Prdx1, Smc4, Fchsd2, Gimap1, Ly75, Cdc25b, Apbb2, Txn1, Rfx2, Plekha5, Grk5, Dennd5b, Ahnak, Carmil1, Ube2s, Itpr1, Tagln2, Phlpp1, Slc4a8, Dnah8, Ablim1, Lrrk2, Dok2, AY036118, Rtl8b, Plcb4, H2afv, Tspan3, Pdzd4, Gpr171, Nck2, Ran, Zfyve9, Sept1, Lyst, Pcna, Ptma, Ranbp1, H2-Q7, Serpina3g, Cd28, H2-Eb2, Ubash3a, Tgm2, Rtl8a, Ccdc162, Tgfbr3, Tex2, Abtb2, Gnb4, Gimap3, Ppp1r16b, Glipr2, Ceacam1, Jak2, Immp2l, Sqstm1, Mcm7, Gm4107, Fgfr1, Cd38, Dennd4a, Cdon, Hist1h1e, Hist1h4d, Gmnn, Zap70, Ftl1, Prtn3, Add3, Gcnt2, Eef1b2, Socs1, Ece1, Smpdl3a, Sh3bgr, Cldn1, Sept6, Slfn5, Chst2, Tespa1, Hmgn3, Ccnd2, Fyn, Arhgap26, Ifi203, Tmpo, Actg1, Arap2, Adgre5, 4930523C07Rik, Herc6, Zdhhc14, Gm8251, Lrba, Mmp25, Bhlhe40, Itga4, Flnb, Ncl, Idi1, Ccrl2, Myo1e, Olfr164, Dusp5, Tnfaip2, Dctpp1, Spc25, Pole, Gm11837, Bbs9, Nasp, Rasgrp2, Cblb, Set, Pdia5, Dhfr, Gimap9, Hspe1, Gi [... truncated]
PC_ 1
Positive: Lyz2, Plbd1, C1qc, C1qb, C1qa, Cst3, Ms4a7, Wfdc17, Ms4a6c, Mctp1
Psap, Tgfbi, Cd72, Hpgd, Cd300a, Clec4a2, Cd86, Cdk14, Clec4b1, C3ar1
Cd14, Cadm1, Mrc1, Ctsb, Ccl6, Tgfbr1, Apoe, Sirpb1a, Mgl2, Creb5
Negative: Atxn1, Timp3, Ptprg, Ly6a, Pde4d, Magi1, St6galnac3, Pard3b, Cobll1, Nbea
Prkca, Ebf1, Shroom4, Etl4, Meis2, Col4a1, Plpp3, Rapgef4, Adgrl2, Pakap.1
Dnm3, Galnt18, Atp1b1, Sntb1, Ldhb, Skap1, Pecam1, Samd12, Ldb2, Stk39
PC_ 2
Positive: Ifitm6, Adgre4, Gpr141, Apoc2, Ear2, Treml4, Ace, Sirpb1c, Plac8, Nr4a1
Cd300e, Eno3, Cd244a, Gm21188, Ldlrad3, F10, S100a4, Hp, Cebpb, Gm15987
Gm5150, Msrb1, Emilin2, Fgr, Arhgef37, Serpinb2, Tppp3, Gda, Gsr, Gm9733
Negative: C1qa, C1qc, C1qb, Ms4a7, Creb5, C3ar1, Cadm1, Cd72, Itga9, Mrc1
Cd14, Col14a1, Negr1, Plxdc2, Aoah, Mgl2, Rbpj, Zmynd15, Ccl12, Agmo
Tgfbr1, Ptchd1, Pf4, Selenop, Rab7b, Mmp13, Hdac9, Psd3, Mir99ahg, Ch25h
PC_ 3
Positive: Adgre4, Apoc2, Ace, Cebpb, Cd300e, Nr4a1, Treml4, Ear2, Ldlrad3, Pla2g7
F10, Gm21188, Apoe, Eno3, Cd300a, Pparg, Lrp1, Lyz2, Ifitm6, Tnfrsf1b
Hp, Gm5150, Sirpb1c, Arhgef37, Fam49a, Gngt2, Ms4a4a, Trem3, Emilin2, Gsr
Negative: Birc5, Top2a, Pclaf, Mki67, Kif11, Cdca8, Tpx2, Nusap1, Knl1, Ccna2
Neil3, Kif4, Cdca3, Cdk1, Ube2c, Cenpe, Aurkb, Hmmr, Kif15, Cenpf
Hist1h1b, Cks1b, Spc24, Prc1, Ckap2l, Ccnb2, Rrm2, Stmn1, Hist1h2ae, Cdca2
PC_ 4
Positive: Flt3, Xcr1, Htr7, Sept3, Ifi205, Snx22, Gcsam, Ffar4, Tlr11, Jaml
Gm43914, Naaa, Fndc7, Clec9a, Gpr141b, Cd209a, Kctd14, Gm36723, H2-Oa, Klri1
Pkib, Srgap3, Rnase6, Hepacam2, Dpp4, Wdfy4, Tbc1d9, Olfm1, Cyp8b1, Rgs18
Negative: Apoe, Pla2g7, Cebpb, Adgre4, Apoc2, Top2a, Birc5, Pf4, Msr1, Nusap1
Kif11, Trem2, Cd300e, Fcrls, Ace, C5ar1, Gm21188, Mki67, Prc1, Ctsb
Hmmr, Lrp1, Kif4, Lgmn, Cenpf, Neil3, Lyz2, Ccna2, Ckap2l, Selenop
PC_ 5
Positive: Cd300e, Treml4, Xcr1, Eno3, Adgre4, Ace, Ear2, Gm43914, Pparg, Cd36
Gcsam, Cadm1, Dusp16, Nr4a1, Ccdc192, Clec9a, Snx22, Havcr2, Tgfbr1, Tlr11
Rap1gap2, Gpr141b, Cdk14, Ifi205, Pglyrp1, Gm17749, Itgal, Itga8, Ldlrad3, Cyp8b1
Negative: Clec10a, Cfp, Cd209a, Ifi30, Olfm1, Ccl9, Ms4a6d, Ms4a4c, Fn1, Trem2
Fcrls, Bex6, S100a4, Ccr2, Ctnnd2, Lgals1, Ccr1, S100a6, Rnd3, Ccl8
Vim, Clec4a2, Klrd1, Wfdc17, Ms4a6c, Kmo, Ccl2, Emb, Gm16553, Pf4
myeloid <- RunUMAP(myeloid, reduction = "pca", dims = 1:40)
16:24:20 UMAP embedding parameters a = 0.9922 b = 1.112
16:24:20 Read 6086 rows and found 40 numeric columns
16:24:20 Using Annoy for neighbor search, n_neighbors = 30
16:24:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:24:20 Writing NN index file to temp file /var/folders/tp/z4kv_q0d5j98lblm0rx44pl40000gr/T//RtmpyaYa3j/file239752ba834b
16:24:20 Searching Annoy index using 1 thread, search_k = 3000
16:24:21 Annoy recall = 100%
16:24:21 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:24:22 Initializing from normalized Laplacian + noise (using RSpectra)
16:24:22 Commencing optimization for 500 epochs, with 259386 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:24:27 Optimization finished
myeloid <- FindNeighbors(myeloid, reduction = "harmony", dims = 1:40)
Computing nearest neighbor graph
Computing SNN
myeloid <- FindClusters(myeloid, resolution = 0.2)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 6086
Number of edges: 226611
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9320
Number of communities: 10
Elapsed time: 0 seconds
myeloid
An object of class Seurat
34285 features across 6086 samples within 2 assays
Active assay: RNA (32285 features, 2000 variable features)
9 layers present: counts.KZ1, counts.KZ2, counts.KZ3, counts.KZ4, data.KZ1, data.KZ2, data.KZ3, data.KZ4, scale.data
1 other assay present: mnn.reconstructed
11 dimensional reductions calculated: pca, umap.unintegrated, cca, umap.cca, rpca, umap.rpca, harmony, umap.harmony, mnn, umap.mnn, umap
table(myeloid$prediction)
arteriole B cells capillary dendritic cell ILC2s macrophage Mixed S1_S2 monocyte Neutrophils
7 166 7 2263 28 2708 10 845 7
NK cells Pericytes Podocytes T cell
1 5 2 37
DimPlot(myeloid, reduction = "umap", group.by = "seurat_clusters")

DimPlot(myeloid, reduction = "umap", group.by = "prediction")

NA
NA
Subset out just the myeloid lineage cells
table(integrated.seurat$prediction)
arteriole B cells capillary dendritic cell fibroblasts ILC2s Intercalated cells
2073 3806 3101 2326 69 1719 602
limb macrophage Mixed S1_S2 monocyte Neutrophils NK cells Pericytes
117 2709 2866 863 561 392 1148
Podocytes Principal cells T cell tubule
157 741 1998 1006
myeloid <- subset(integrated.seurat, prediction %in% c("dendritic cell", "macrophage", "monocyte"))
DimPlot(myeloid, reduction = "umap.harmony", group.by = "prediction", label=TRUE)

# re-cluster the subset (NOTE THE OBJECT CANNOT BE JOINED ALREADY OR IT WON'T PROPERLY CLUSTER)
DefaultAssay(myeloid) <- "RNA"
myeloid <- FindVariableFeatures(myeloid)
Finding variable features for layer counts.KZ1
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.KZ2
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.KZ3
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts.KZ4
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
myeloid <- RunPCA(myeloid)
Warning in PrepDR5(object = object, features = features, layer = layer, :
The following features were not available: S100a10, Airn, Rabgap1l, Mllt3, Prdx1, Setbp1, Ly75, Fchsd2, Plekha5, Ahnak, Grk5, Tubb4b, Tagln2, Apbb2, Txn1, Dok2, Itpr1, Smc4, Rfx2, Slc4a8, AY036118, Rtl8b, Tspan3, Phlpp1, H2-Eb2, Lrrk2, Abtb2, Zfyve9, Tgfbr3, Nck2, Jak2, Lyst, Gpr171, Mcm7, Cdon, Ptma, Rtl8a, Glipr2, Ranbp1, Immp2l, Rai14, H2afv, Fgfr1, Tgm2, Gcnt2, Ran, Sept6, Cd38, Socs1, Ece1, Gm11837, Plcb4, Cldn1, Gm4107, Ccnd2, Gmnn, Add3, Flnb, Dennd4a, Ube2s, Smpdl3a, Carmil1, Ccrl2, Pdia5, Eef1b2, Ftl1, Lrba, Slfn5, Dusp5, Bbs9, Pcna, Hmgn3, Slc44a4, Actg1, Lima1, Ccne2, Idi1, Ifnlr1, Arhgap5, Bhlhe40, Arhgap26, Mmp25, Adgre5, Ifi47, Cdk2ap2, Myo1e, Rasgrp2, Ifi203, Tnfaip2, Cpne8, Itga4, Pcgf5, Dctpp1, Cblb, Hmgcs1, Cyth3, Serpina3g, Cenpm, Wwox, H2-Q7, Tmpo, Psmb9, Pole, Lrrc1, Rab30, Tiam2, Malt1, Dennd5b, Itsn1, Zdhhc14, Ppef2, Pianp, Kcnip3, Nfkbia, Rbpms, Ung, Xylt1, Kcnn4, Gm26535, Acyp2, Tmlhe, Herc6, Sub1, Atrnl1, Anp32b, Fry, Hspa8, Itga1, Net1, Chst11, Myo9a, Ccdc88c [... truncated]
PC_ 1
Positive: Atxn1, Ly6a, Pde4d, Pard3b, Prkca, Ebf1, Shroom4, Meis2, Rapgef4, Pakap.1
Adgrl2, Sntb1, Skap1, Pecam1, Pbx1, Stk39, Esrrg, Ptprb, Tox, Prkcq
Glis3, Spp1, Plcb1, Bcl11b, Plpp1, Ntn4, Heg1, Tnik, Nr3c2, Flt1
Negative: Lyz2, Aif1, C1qc, C1qb, Plbd1, C1qa, Ms4a7, Trf, Cst3, Wfdc17
Alox5ap, Ms4a6c, Fcgr1, Mctp1, Cd72, Tgfbi, Psap, Hpgd, Clec4a2, C3ar1
Cd14, Clec4b1, Cd86, Mrc1, Cd300a, Cadm1, Ctsb, Cdk14, Ccl6, Mgl2
PC_ 2
Positive: Ifitm6, Adgre4, Gpr141, Apoc2, Ear2, Treml4, Ace, Sirpb1c, Nr4a1, Cd300e
Plac8, Eno3, Gm21188, F10, Cd244a, Ldlrad3, Hp, Cebpb, Gm5150, S100a4
Fgr, Msrb1, Emilin2, Gm15987, Arhgef37, Serpinb2, Tppp3, Gda, Gsr, Gm9733
Negative: C1qa, C1qc, C1qb, Ms4a7, Slco2b1, Creb5, Cadm1, Cd72, C3ar1, Itga9
Mrc1, Cd14, Plxdc2, Negr1, Mgl2, Aoah, Rbpj, Trf, Zmynd15, Aif1
Ccl12, Hs3st3a1, Agmo, Ptchd1, Pf4, Tgfbr1, Rab7b, Selenop, Psd3, Hdac9
PC_ 3
Positive: Flt3, Xcr1, Sept3, Ifi205, Htr7, Snx22, Gcsam, Ffar4, Tlr11, Jaml
Naaa, Cd209a, Kctd14, Tnni2, Fndc7, Olfm1, Gpr141b, Gm36723, H2-Oa, Pkib
Klri1, Gm43914, Srgap3, Clec9a, Hepacam2, Tbc1d9, Wdfy4, Dpp4, Rgs18, Cyp8b1
Negative: Apoe, Adgre4, Pla2g7, Apoc2, Cebpb, Ace, Cd300e, Ctsb, C5ar1, Gm21188
Ldlrad3, Lyz2, Msr1, Pparg, Lrp1, Ms4a7, Nr4a1, Selenop, Tnfrsf1b, F10
Lgmn, Ms4a4a, Eno3, Hpgd, Treml4, C1qc, C1qa, Pilra, Gngt2, C1qb
PC_ 4
Positive: Birc5, Pclaf, Top2a, Mki67, Cdca8, Kif11, Nusap1, Aurkb, Neil3, Ccna2
Cdca3, Knl1, Ckap2l, Kif15, Cdk1, Spc24, Tpx2, Stmn1, Cenpe, Kif4
Hmmr, Hist1h2ae, Pbk, Cks1b, Ube2c, Bub1, Prc1, Sgo1, Cenpf, Cdca2
Negative: Gm43914, Xcr1, Flt3, Tlr11, Clec9a, Snx22, Gcsam, Rnase6, Fndc7, Cadm1
Htr7, Ffar4, Ifi205, Mctp1, Sept3, Slamf7, Wdfy4, Hepacam2, Gpr141b, Havcr2
Dpp4, Cyp8b1, Gm36723, Adam23, Tbc1d9, Dnase1l3, Itga8, Gm45238, Ptchd1, Plbd1
PC_ 5
Positive: Clec10a, F13a1, Cd209a, Cfp, Ifi30, Olfm1, Fn1, Ccl9, Ms4a4c, Ms4a6d
Trem2, Ccr2, S100a4, Fcrls, Bex6, Ccr1, Chil3, Rnd3, Vcan, Ccl8
Vim, Ctnnd2, Lgals1, Kmo, Klrd1, Wfdc17, Klri1, Ms4a6c, Gm16553, Emb
Negative: Cd300e, Treml4, Eno3, Adgre4, Ear2, Ace, Pparg, Dusp16, Nr4a1, Ccdc192
Cd36, Cadm1, Xcr1, Tgfbr1, Rap1gap2, Havcr2, Pglyrp1, Cdk14, Gm17749, Gm43914
Itgal, Ldlrad3, Dnah12, Clec9a, Gcsam, Slc12a2, Fam49a, Apoc2, Itga8, Skint3
myeloid <- RunUMAP(myeloid, reduction = "pca", dims = 1:40)
16:24:36 UMAP embedding parameters a = 0.9922 b = 1.112
16:24:36 Read 5898 rows and found 40 numeric columns
16:24:36 Using Annoy for neighbor search, n_neighbors = 30
16:24:36 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:24:36 Writing NN index file to temp file /var/folders/tp/z4kv_q0d5j98lblm0rx44pl40000gr/T//RtmpyaYa3j/file23975301db5b
16:24:36 Searching Annoy index using 1 thread, search_k = 3000
16:24:37 Annoy recall = 100%
16:24:37 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
16:24:38 Initializing from normalized Laplacian + noise (using RSpectra)
16:24:38 Commencing optimization for 500 epochs, with 250938 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:24:43 Optimization finished
myeloid <- FindNeighbors(myeloid, reduction = "harmony", dims = 1:40)
Computing nearest neighbor graph
Computing SNN
myeloid <- FindClusters(myeloid, resolution = 0.2)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5898
Number of edges: 219144
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9291
Number of communities: 10
Elapsed time: 0 seconds
myeloid
An object of class Seurat
34285 features across 5898 samples within 2 assays
Active assay: RNA (32285 features, 2000 variable features)
9 layers present: counts.KZ1, counts.KZ2, counts.KZ3, counts.KZ4, data.KZ1, data.KZ2, data.KZ3, data.KZ4, scale.data
1 other assay present: mnn.reconstructed
11 dimensional reductions calculated: pca, umap.unintegrated, cca, umap.cca, rpca, umap.rpca, harmony, umap.harmony, mnn, umap.mnn, umap
table(myeloid$harmony.clusters)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
3004 0 3 0 0 9 1192 31 1 0 0 0 1 688 15 19 0 531 0 401 0 0 2 0 0 0 1
27
0
DimPlot(myeloid, reduction = "umap", group.by = "seurat_clusters")

DimPlot(myeloid, reduction = "umap", group.by = "prediction")

sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] future_1.49.0 dplyr_1.1.4 here_1.0.1 ggplot2_3.5.2
[5] Seurat_5.2.1 SeuratObject_5.0.2 sp_2.1-4 SingleCellExperiment_1.26.0
[9] SummarizedExperiment_1.34.0 Biobase_2.64.0 GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[13] IRanges_2.38.1 MatrixGenerics_1.16.0 matrixStats_1.5.0 S4Vectors_0.42.1
[17] BiocGenerics_0.50.0
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_2.0.0 magrittr_2.0.3 spatstat.utils_3.1-2
[6] rmarkdown_2.29 farver_2.1.2 ragg_1.3.3 zlibbioc_1.50.0 vctrs_0.6.5
[11] ROCR_1.0-11 spatstat.explore_3.3-4 htmltools_0.5.8.1 S4Arrays_1.4.1 SparseArray_1.4.8
[16] sass_0.4.10 sctransform_0.4.1 parallelly_1.45.0 bslib_0.9.0 KernSmooth_2.23-26
[21] htmlwidgets_1.6.4 ica_1.0-3 plyr_1.8.9 cachem_1.1.0 plotly_4.10.4
[26] zoo_1.8-12 igraph_2.1.4 mime_0.13 lifecycle_1.0.4 pkgconfig_2.0.3
[31] Matrix_1.7-2 R6_2.6.1 fastmap_1.2.0 GenomeInfoDbData_1.2.12 fitdistrplus_1.2-2
[36] shiny_1.10.0 digest_0.6.37 colorspace_2.1-1 patchwork_1.3.0 rprojroot_2.0.4
[41] tensor_1.5 RSpectra_0.16-2 irlba_2.3.5.1 textshaping_1.0.0 labeling_0.4.3
[46] progressr_0.15.1 spatstat.sparse_3.1-0 httr_1.4.7 polyclip_1.10-7 abind_1.4-8
[51] compiler_4.4.1 withr_3.0.2 fastDummies_1.7.5 MASS_7.3-64 DelayedArray_0.30.1
[56] tools_4.4.1 lmtest_0.9-40 httpuv_1.6.16 future.apply_1.11.3 goftest_1.2-3
[61] glue_1.8.0 nlme_3.1-167 promises_1.3.3 grid_4.4.1 Rtsne_0.17
[66] cluster_2.1.8 reshape2_1.4.4 generics_0.1.3 gtable_0.3.6 spatstat.data_3.1-4
[71] tidyr_1.3.1 data.table_1.17.0 utf8_1.2.4 XVector_0.44.0 spatstat.geom_3.3-5
[76] RcppAnnoy_0.0.22 ggrepel_0.9.6 RANN_2.6.2 pillar_1.10.1 stringr_1.5.1
[81] spam_2.11-1 RcppHNSW_0.6.0 later_1.4.2 splines_4.4.1 lattice_0.22-6
[86] survival_3.8-3 deldir_2.0-4 tidyselect_1.2.1 miniUI_0.1.1.1 pbapply_1.7-2
[91] knitr_1.50 gridExtra_2.3 scattermore_1.2 xfun_0.52 stringi_1.8.4
[96] UCSC.utils_1.0.0 yaml_2.3.10 lazyeval_0.2.2 evaluate_1.0.4 codetools_0.2-20
[101] tibble_3.2.1 cli_3.6.5 uwot_0.2.2 systemfonts_1.2.1 xtable_1.8-4
[106] reticulate_1.42.0 jquerylib_0.1.4 munsell_0.5.1 Rcpp_1.0.14 globals_0.18.0
[111] spatstat.random_3.3-2 png_0.1-8 spatstat.univar_3.1-1 parallel_4.4.1 dotCall64_1.2
[116] listenv_0.9.1 viridisLite_0.4.2 scales_1.3.0 ggridges_0.5.6 purrr_1.0.4
[121] crayon_1.5.3 rlang_1.1.6 cowplot_1.1.3
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